Data Scientist

Position: Data Scientist
Location: Trivandrum (on-site)
Experience Required: 5 Years
Budget: Upto 30 LPA


About the Role

The role requires a strong focus on data analysis machine learning model development and fraud detection across large-scale datasets. The ideal candidate collaborates closely with engineering and product teams to build scalable and reliable machine learning solutions that support data-driven decision-making. Exposure to model development feature engineering experiment tracking and modern MLOps practices is a strong advantage

Key Responsibilitie

• Design develop and refine high-performance Fraud Prevention models using Python and Gradient Boosting frameworks such as XGBoost LightGBM or CatBoost.
• Manage the complete machine learning lifecycle including data extraction feature engineering model training evaluation and deployment support.
• Conduct data research behavioural analysis and performance benchmarking on production datasets.
• Write and optimize SQL queries to extract and analyse data from PostgreSQL databases for model development and validation.
• Utilize MLflow for experiment tracking model versioning and ensuring reproducibility across development stages.
• Maintain code integrity and collaborative workflows using Git and Bitbucket.
• Work within Linux environments and utilize shell scripting (Bash) to automate workflows and operational tasks.
• Develop visualizations and analytical insights using data visualization tools.
• Collaborate with cross-functional teams to improve model performance and data-driven decision making.
• Ensure data privacy security and compliance best practices while working with production data.

Required Skills

• 5–8 years of overall experience in Data Science Machine Learning or related roles.
• 3–5 years of hands-on experience in Python-based data science and machine learning.
• Strong proficiency in Python and data science libraries such as Pandas NumPy and Scikit-learn.
• 1–2 years of experience working with Gradient Boosting frameworks such as XGBoost LightGBM or CatBoost.
• Strong knowledge of SQL and PostgreSQL for data extraction and analysis.
• Hands-on experience with MLflow for experiment tracking and model versioning.
• Experience with Jupyter Notebook or JupyterHub for model experimentation and data exploration.
• Proficiency in Git and Bitbucket for version control and collaborative development.
• Familiarity with Linux/Unix environments and basic Shell scripting.
• Understanding of machine learning techniques including classification anomaly detection and feature engineering.
• Knowledge of data visualization tools such as Plotly Matplotlib or Seaborn.
• Strong analytical thinking problem-solving ability and attention to detail.
• Good communication and collaboration skills.

Added Advantage

• Experience working with large datasets or big data technologies such as Spark or Dask.
• Prior experience in Fintech Banking or Cybersecurity domains.
• Understanding of MLOps concepts including model deployment and monitoring in production environments.
• Familiarity with package management tools such as Conda Pip or virtual environments.
• Knowledge of data privacy and security best practices when handling production data

Required Skills:

Data scientist python machine learning ML Algorithms sql git